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processes
Review
Practical Solutions for Specific Growth Rate ControlSystems in Industrial Bioreactors
Vytautas Galvanauskas * , Rimvydas Simutis, Donatas Levišauskas and Renaldas Urniežius
Department of Automation, Kaunas University of Technology, Kaunas 51367, Lithuania;[email protected] (R.S.); [email protected] (D.L.); [email protected] (R.U.)* Correspondence: [email protected]; Tel.: +370-37-300-291
Received: 27 August 2019; Accepted: 30 September 2019; Published: 2 October 2019�����������������
Abstract: This contribution discusses the main challenges related to successful application of automaticcontrol systems used to control specific growth rate in industrial biotechnological processes. It isemphasized that, after the implementation of basic automatic control systems, primary attentionshall be paid to the specific growth rate control systems because this process variable critically affectsthe physiological state of microbial cultures and the formation of the desired product. Therefore,control of the specific growth rate enables improvement of the quality and reproducibility ofthe biotechnological processes. The main requirements have been formulated that shall be met tosuccessfully implement the specific growth rate control systems in industrial bioreactors. The relativelyeasy-to-implement schemes of specific growth rate control systems have been reviewed and discussed.The recommendations for selection of particular control systems for specific biotechnological processeshave been provided.
Keywords: biotechnological processes; bioreactor control; specific growth rate control; batch-to-batchreproducibility
1. Introduction
Biotechnological processes play an increasingly important role in modern industry and healthsectors. Many of the important active pharmaceutical ingredients are recombinant therapeuticproteins produced by the cultivation of genetically modified microorganisms or mammalian cells inbioreactors. These biotechnological processes are highly nonlinear and non-stationary. Therefore,modeling and control of the above bioprocesses are complicated control engineering tasks, especially inindustrial recombinant protein production processes, in which high safety requirements and operationalrestrictions must be secured [1,2]. The goal of this contribution is to review and recommend practicaland easily implementable control system schemes for biomass specific growth rate (further referredto as SGR or µ) control in industrial bioreactors. The recommendations are based on an analysisof the existing SGR control solutions and availability of the control schemes suitable for practicalimplementation in industrial bioreactors. The specific growth rate µ (1/h), is defined as the ratio of thecell’s absolute growth rate and the amount of cells:
µ =dXdt
1X
(1)
where X = xV (g) is the cell (biomass) amount; x (g/L) is the cell (biomass) concentration; and V (L) isthe cultivation broth volume. The SGR is the most important variable in biotechnological processes,which influences the physiological state of microbial culture, production of cell biomass and desiredproducts, and quantity and quality of products [3–8].
Processes 2019, 7, 693; doi:10.3390/pr7100693 www.mdpi.com/journal/processes
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The development of relatively simple and reliable methods for SGR monitoring and controlin industrial bioreactors is one of the most important control engineering tasks for successfulimplementation of the Process Analytical Technology (PAT) framework in bioengineering [9,10].However, to properly exploit the benefits of SGR control systems in microbial and mammalian cellcultivation processes, basic bioprocess variables (temperature, pH, dissolved oxygen concentration,etc.) need to be controlled by commonly available and well-functioning control systems. Unfortunately,in many cases these systems do not ensure sufficient control quality [1,11,12] allowing to furtherproceed with SGR monitoring and control.
This paper is structured as follows: In Section 2, importance of the control quality of thebasic control systems in the biotechnological processes is analyzed. Section 3 introduces importantpreconditions for implementation of SGR control systems in industrial bioreactors. Section 4 expands onstrategies for SGR control suitable for industrial bioreactors. Finally, the authors give recommendationsfor application of the discussed SGR control solutions in various biotechnological processes.
2. Quality of Basic Control Systems in Industrial Bioreactors
The performance quality of automatic control systems for basic process variables is still lowin most industrial microbial and mammalian cell cultivation processes [1,11]. Despite the factthat sophisticated control strategies for microbial cultivation processes are widely discussed in theacademic community and research papers [2,13–15], the authors’ experience shows that, at present,only simple, conventional automatic control systems are realized in the majority of industrial-scale(bio)reactors [11,16]. This situation is related to a common underestimation of the control systems’importance in improving the productivity and quality of the biotechnological processes. It is alsorelated to the relatively high costs of implementation and maintenance of these advanced controlsystems and the resultant low acceptance of these systems by plant managers.
Bioreactors are the key operation units in biochemical and biopharmaceutical processes,in which the basic control systems attempt to control the cultivation environment outside of thecell. The commonly controlled variables of the cells’ environment are temperature, pressure, pH,and dissolved oxygen concentration. The basic feedback control systems of industrial bioreactors forcontrolling bacterial cell cultures that produce biopharmaceutical products are presented in Figure 1.The temperature controller manipulates the flow rate of cooling water in the jacket. The pressure insidethe bioreactor is controlled by manipulation of the off-gas flow rate. The pH controller manipulates theflow rate of ammonia solution (usually, the acid solution does not need to be added to bacterial cellculture cultivations, unless compensation of base excess is required). The dissolved oxygen controlleroutput is split to manipulate the air flow rate and the agitation speed (at high cell density cultivationthe air flow may be enriched by additional oxygen).
Today, the most important industrial cultivations of microbial and mammalian cells are carried outin the fed-batch mode. In fed-batch processes, one or more substrates are fed into the bioreactor duringthe process. The product remains in the bioreactor until the end of the cultivation cycle. Fed-batchprocesses overcome substrate inhibition and overflow effects. Such an operational mode allows a highcell density and product concentrations to be achieved [6]. By controlling the substrate feeding rate,the optimal conditions for the biotechnological process can be secured.
To realize the bacterial growth rate control systems, efficient glucose feeding algorithms needto be implemented, and in the mammalian cell cultivation processes, additionally, the feeding rateof glutamine needs to be controlled. It is important to note that modern industrial bioreactors areequipped with inexpensive and reliable devices to measure the composition of aeration gas in the inletand outlet (fraction of O2, CO2) and the molar flow rate, Q. Hence, the oxygen uptake rate (OUR) andthe carbon dioxide production rate (CPR) can be calculated from the online measurements as follows:
OUR = Q(Oin
2 −Oout2
), (2)
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CPR = Q(COout
2 −COin2
). (3)
The above measurements allow an online estimation of the important process variables, OURand CPR, during bacterial and mammalian cell cultivation processes [17,18]. Because of the lowercell density and respiratory intensity, OUR and CPR measurements based on the off-gas compositionmay cause larger measurement errors in mammalian cell cultivation processes. As an alternativetechnique, an OUR estimation using dissolved oxygen (DO) measurements may also be applied [19].The OUR and CPR are the most important variables for indirect monitoring of the biomass growth ratein industrial bioreactors, as they comprehensively reflect the physiological state and metabolic activityof the aerobic biotechnological processes [1–3,11,20].
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The above measurements allow an online estimation of the important process variables, OUR and CPR, during bacterial and mammalian cell cultivation processes [17,18]. Because of the lower cell density and respiratory intensity, OUR and CPR measurements based on the off-gas composition may cause larger measurement errors in mammalian cell cultivation processes. As an alternative technique, an OUR estimation using dissolved oxygen (DO) measurements may also be applied [19]. The OUR and CPR are the most important variables for indirect monitoring of the biomass growth rate in industrial bioreactors, as they comprehensively reflect the physiological state and metabolic activity of the aerobic biotechnological processes [1–3,11,20].
Figure 1. Basic control system loops for a typical microbial cultivation process.
Good performance of the basic control systems improves the batch-to-batch reproducibility/repeatability of the processes [11,21]. The other advantage of a well-controlled bioreactor is the possibility to run the bioreactor at higher capacity or with better efficiency by operating the process closer to physical constraints. Good reproducibility is also an important condition for possible process improvements and modifications, as improvement in reproducibility by means of well-operating control systems allows a reduction in the number of expensive and time-consuming experiments required to compare the performance indices of the modified processes and to optimize the controlled technological regime [1].
The proportional-integral-derivative (PID) controllers are predominant controllers used in the basic control systems of microbial and mammalian cell cultivation processes. Quality of the bioprocess control depends on the complexity of the process dynamics, the process variable measurement noise and errors, tuning of the system controllers, and performance accuracy of the executive devices (valves and speed drives). Dynamics of the particular biotechnological parameter control process can be characterized by three resulting dynamic parameters: dead time, time constant and process gain. These parameters are commonly used to determine the tuning parameters of a PID controller. Control quality of the bioreactor operation mode critically depends on how well the controllers are set up and tuned to deal with the sources of the process variability [1,2,11,13]. Because
Figure 1. Basic control system loops for a typical microbial cultivation process.
Good performance of the basic control systems improves the batch-to-batch reproducibility/
repeatability of the processes [11,21]. The other advantage of a well-controlled bioreactor is thepossibility to run the bioreactor at higher capacity or with better efficiency by operating the processcloser to physical constraints. Good reproducibility is also an important condition for possible processimprovements and modifications, as improvement in reproducibility by means of well-operatingcontrol systems allows a reduction in the number of expensive and time-consuming experimentsrequired to compare the performance indices of the modified processes and to optimize the controlledtechnological regime [1].
The proportional-integral-derivative (PID) controllers are predominant controllers used in thebasic control systems of microbial and mammalian cell cultivation processes. Quality of the bioprocesscontrol depends on the complexity of the process dynamics, the process variable measurement noiseand errors, tuning of the system controllers, and performance accuracy of the executive devices (valvesand speed drives). Dynamics of the particular biotechnological parameter control process can becharacterized by three resulting dynamic parameters: dead time, time constant and process gain.
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These parameters are commonly used to determine the tuning parameters of a PID controller. Controlquality of the bioreactor operation mode critically depends on how well the controllers are set upand tuned to deal with the sources of the process variability [1,2,11,13]. Because of the nonlinearityand nonstationarity of the bioprocesses, proper tuning of the controllers requires appropriate efforts.The performance of PID controllers with fixed tuning parameters are not sufficiently accurate becauseof the significant variations in the process’ dynamics. Consequently, various approaches have beenproposed to tune the PID controller parameters in microbial cultivation processes under time-varyingoperating conditions, including gain-scheduling methods [12,22–24], first-principle models [25],tendency models [26], rule-based fuzzy systems [13], and other techniques [1,2,14,15]. The proposedapproaches give a sound theoretical and practical basis to implement adaptive control schemes inbioreactor systems and show that implementation of the adaptive algorithms in basic control systemscan significantly increase the performance of the systems. Gain-scheduling methods and tendencymodels are the most appropriate solutions for improving the quality of basic control systems in microbialcultivation processes because they are relatively simple to implement and pose low requirements onmodel complexity. The advantages have been extensively discussed and substantiated in previousstudies [12,22–24,26]. An important task now is to broaden implementation of these algorithms inindustrial bioreactors.
Well-functioning basic control systems create opportunities for further process improvements andalso for implementation of the SGR control systems in bioreactors [1,11]. Development of relativelysimple solutions to control the specific growth rate in fed-batch processes remains a timely andimportant task in view of implementing the PAT framework in industrial biotechnological processes.
3. Preconditions for Implementation of SGR Control Systems in Industrial Bioreactors
The basic requirements for SGR control systems designed to control microbial and mammaliancell cultivation processes can be formulated as follows:
• The systems should be as simple as possible and intuitive for the user. Process operators withoutspecial modeling/control knowledge should be able to supervise these systems.
• The systems must be based on measurement and control equipment that is currently used standardequipment in industrial bioreactors.
• Development time, cost, and benefits of the systems must be attractive to potential users.
According to the above requirements, most of the solutions for SGR control systems presentedin scientific literature [3,6] are not attractive enough for industrial implementation. More complexmonitoring and control systems, even if equipped with easy-to-use interfaces, may require retuning,model identification, and maintenance tasks in case the operational modes or microbial cultures havebeen changed. Often these tasks cannot be carried out by the biotechnology companies alone and maycause additional expenses for outsourcing and production delays. In the authors’ opinion, this is themain reason SGR control systems have rarely been used in industrial bioreactors so far.
In this contribution, the authors provide an overview of those SGR control systems that meetthe aforementioned requirements. In most widespread control systems, SGR is usually controlledby manipulating the substrate feeding rate [6,27]. In recombinant protein production processes,the temperature of the medium is also used to control cell growth [28]. Despite that the growthrate could be controlled (e.g., by manipulating the dissolved oxygen concentration in cultivationmedium [29], temperature of the medium [28] or pH), to date these techniques have not been sufficientlyinvestigated and have not been widely implemented in industrial practice [11].
When the growth rate is controlled by manipulating the substrate feeding rate, the substrateconcentration in cultivation medium remains relatively low [30]. This allows avoiding productionof the overflow metabolites in some of the most important microbial expression systems, so-calledCrabtree-positive organisms, such as S. cerevisiae and E. coli. The presence of the overflow metabolites,
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such as acetate or ethanol, frequently leads to inhibition of both the biomass growth and formation ofthe proteins.
To ensure controllability and batch-to-batch reproducibility, the SGR needs to be controlled duringthe process at a level that is lower than the maximum SGR [11]. The maximum available SGR isobserved during particular growth phases of the process, when the growth is not limited by substrateconcentration [12,31], and depends on the specific culture, medium composition, concentrations ofbiomass and metabolites, as well as the oxygen transfer capabilities of the bioreactor. It is worthmentioning that direct control of the substrate concentration in bioreactor at the set-point does notguarantee that a constant SGR will be kept. This is because [11]:
• Cell growth at a limited rate occurs under low substrate concentrations. Because of this, onlinemeasurements, calibration of the measuring devices, and control of the substrate concentrationare difficult to implement in industrial bioreactors.
• Sensor readings of the substrate concentration reflect only the local substrate concentrationaround the sensor, which may significantly differ from the average concentration in the bioreactor.Therefore, the substrate concentration control system is not able to control the SGR in the entirecultivation medium.
In the majority of recombinant protein production processes, the control objective is to maximize theamount of target protein at the end of the process while maintaining high batch-to-batch reproducibility.To achieve this goal, two steps most often are implemented for SGR control:
• During the first stage of the process, the SGR is kept at a trajectory that is 10–15% below the maximumavailable SGR.
• During the second stage, the SGR is kept at a trajectory that leads to the maximum specificproduction rate of the target product. Usually, the level of the SGR kept at this phase is significantlylower compared to that maintained at the first stage.
SGR control systems can be realized using open-loop and closed-loop control systems [6,11].In the following sections, the authors analyze and evaluate SGR control systems that are best suited forindustrial applications. The analyzed and evaluated control solutions are ordered in this review bytheir complexity (i.e., starting with the simplest open-loop systems and ending up with the controlsystems that employ cascade control schemes and SGR estimators).
4. Schemes for SGR Practical Control Systems
4.1. Open-Loop SGR Control Systems
The majority of industrial fed-batch microbial cultivation processes are operated using open-loopSGR control systems [11], in which the time profile of the substrate feeding rate is calculated usingsimple mass-balance models and a desired time profile of the SGR during the process. The desiredSGR values, µset, can be described by the following equation:
µset =
{µset1 = (0.85 . . . 0.90)µmax f or growth phase,
µset2 = µopt f or production phase.(4)
Based on the desired set values for SGR, the corresponding substrate feeding rate can be determined.Accumulation of the total biomass during cultivation and the substrate feeding rate for both stages ofthe process can be estimated from simple mass-balance equations:
dXdt
= µsetiX, i = 1, 2. (5)
The amount of biomass accumulated in the growth stage can be calculated from the equation
X(t) = X0eµset1 t, (6)
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and the amount of biomass accumulated in the production stage can be calculated from the equation
X(t) = X0eµset1 tg · eµset2 (t−tg), (7)
where tg (h) is the end time of the growth stage.Using the predicted time trajectories of the biomass accumulation, X(t), the substrate feeding rate
F1(t) in the growth stage can be derived from the dynamic mass-balance equation for the substrateunder steady-state conditions [2,6,8,20,30], which results in the following equation
F1(t) =X0eµset1 tµset1
Yxs1SF, (8)
and the substrate feeding rate F2(t) in the production stage can be estimated from the equation
F2(t) =X0eµset1 tgeµset2 (t−tg)µset2
Yxs2SF, (9)
where X0 (g) is the total amount of biomass in the bioreactor at the beginning of cultivation process;X(t) (g) is the time trajectory of the total biomass accumulated during the process; SF (g/L) is theconcentration of the substrate in the feeding solution; and Yxs1 and Yxs2 (g/g) are the yields of biomasson substrate in the growth and production phases, respectively. In substrate-limited processes,the substrate concentration in the bioreactor is low. Therefore, this concentration is not taken intoaccount in Equations (8) and (9).
When the SGR control algorithm based on Equations (8) and (9) is developed, implementation ofthe control system is straightforward. For this purpose, only an actuator to dose the feeding substrateto the bioreactor is needed. For some recombinant protein production processes, the yield of biomasson substrate can be different in the growth (Yxs1) and the production stages (Yxs2). In such cases, theyields must be identified from experimental data for the particular process phase and must be takeninto account when using Equations (8) and (9) to estimate the substrate feeding rates. Additionally,µmax and µopt may vary during the process because of the increasing concentrations of metabolites,biomass, and other process variables. In this case, µmax(t) and µopt(t) should be presented as timeprofiles, and a numerical integration procedure to predict the biomass growth and substrate feedingtime profiles needs to be applied.
The substrate feeding time profiles estimated from Equations (8) and (9) can be directlyused for implementing the open-loop SGR control systems in various biotechnologicalprocesses [6,7,22,30,32,33]. Certainly, more sophisticated bioprocess models and optimizationprocedures can be used to determine the feeding rate control algorithms in open-loop SGR controlsystems. These methods are widely reviewed and analyzed in many research and academicpapers [3,6,8,27,34,35]. However, implementation of more sophisticated procedures in industrialbioprocesses requires specific knowledge in process modeling and efforts to develop more accuratemodels. Consequently, application of complex methods in industrial environment is not a commonplace.
The SGR open-loop control systems based on Equations (8) and (9) are easy to implement and donot require additional measurements. On the other hand, open-loop systems do not compensate forprocess disturbances. Consequently, possible variations in the substrate concentration of the feedingsolution or deviations of the feeding flow rate are not compensated. These disturbances can decreasethe performance of the biotechnological process. In the next sections, the authors analyze and providerelatively simple and already existing solutions to overcome these problems.
4.2. SGR Control Systems Based on CPR/OUR Estimations
Reliability and accuracy of SGR control can be increased by employing closed-loop control systems.One of the simplest closed-loop SGR control systems is proposed in Reference [27]. Here, based on a
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simplified assumption that the carbon dioxide production rate (CPR) during the process is in a linearrelationship with the biomass growth rate
CPR(t) = αµ(t)X(t), (10)
the specific growth rate µ can be estimated using the following equation
µ(t) =CPR(t)∫ t
0 CPR(τ)dτ, (11)
where α is a model parameter, and τ is the integration time variable.If real-time estimation of SGR is available, feedback control systems can be developed to
automatically track a desired SGR time profile by manipulating the substrate feeding rate.Results of the SGR control obtained in Reference [36] show that an acceptable control quality can
be obtained by applying a typical control system based on PI controllers. To achieve better controlquality, it is straightforward to adapt the controller parameters to the time-varying dynamics of thecontrolled process by applying gain-scheduling algorithms mentioned in Section 2 and using theCPR signal as a scheduling variable. The main drawback of the analyzed control approach is that,during SGR estimation, an assumption is made that the CPR during the process is proportional tothe absolute biomass growth rate. In fact, it is known that more accurate results may be achievedif the Luedeking–Piret-type relationship is applied to correlate the CPR and the biomass growthrate [20,37]. This relationship additionally takes into account the CPR fraction that is related to themaintenance of the cell’s vital functions and accounts for a significant part of the total CPR (for instance,in high-cell-density bacterial cultivation processes). Figure 2 shows the simulated trajectories of thebiomass growth and the CPR of the recombinant E. coli cultivation process in a 1 m3 volume bioreactoras well as the comparison of the actual and the estimated SGRs. The latter is calculated from Equations(10) and (11). The actual CPR of the process is modeled using the equation
CPR(t) = αµ(t)X(t) + βX(t), (12)
where parameter β determines the CPR fraction related to maintenance of the cell’s vital functions.
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In the next sections, more complex control systems are discussed that overcome the above shortcomings.
(a) (b)
Figure 2. Simulated trajectories of the biomass growth and carbon dioxide production rate (CPR) (a), and the trajectories of the real specific growth rate (SGR) and that estimated from Equation (7) (b) in a typical recombinant E. coli cultivation process in a 1 m3 bioreactor.
(a) (b)
Figure 3. Block-scheme of the SGR control system (a) and the simulation results of the system performance (b). Reproduced with permission from D. Levišauskas, Biotechnology Letters; published by Springer Nature, 2001.
4.3. SGR Control Systems Based on CPR/OUR Estimations and the Mass of CO2/O2 Produced/Consumed During Cultivation
Robust control of the SGR is a crucial problem when designing an efficient process, in which the SGR is to be controlled at the value μset < μmax in order to secure reproducibility of the processes. However, the already discussed SGR closed-loop control systems have two shortcomings: (a) for system implementation, an online estimation of the μ-values is required, and (b) high batch-to-batch reproducibility is not guaranteed. For example, if disturbances occur during a process (e.g., variation in the initial amount of biomass X0) or in the instrumentation (e.g., if the substrate feeding is shortly interrupted), they cause slight deviations in the biomass growth trajectory from the desired trajectory, and such an offset cannot be eliminated later on, even if the controller exactly tracks the predefined μ-profile. An approach to cope with the disturbances that cause process reproducibility problems is proposed in Reference [40]. In this work, a desired SGR time profile μset(t), the initial amount of biomass X0, and Equation (1) were used to estimate the biomass growth time profile X(t) during the process. If the biomass growth profile X(t) can be tightly controlled by manipulating the substrate feeding rate, the corresponding SGR profile will follow the desired μset(t) profile. This control system is more robust as compared to direct SGR control systems, as the short-term disturbances that occur
Figure 2. Simulated trajectories of the biomass growth and carbon dioxide production rate (CPR) (a),and the trajectories of the real specific growth rate (SGR) and that estimated from Equation (7) (b) in atypical recombinant E. coli cultivation process in a 1 m3 bioreactor.
In the simulation experiment, values of the parameters α and β were used that are typical forrecombinant E. coli cultivation processes (α = 0.9 (gCO2/gX), and β = 0.1 (gCO2/(gX·h))), induction at
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t = 8 h) [38]. The simulation results, presented in Figure 2, show that the estimated SGR deviationfrom the real trajectory increases with an increasing amount of biomass (the estimated rate at theend of the process is 0.05 (1/h) higher than the real one). Hence, it is advantageous to introduceempiric correlations correct the estimated SGR when applying this method in high-density cultivationprocesses. The magnitude of correction should be defined from earlier cultivation experiments.
To estimate SGR, OUR data can also be used. However, the measurements related to OURestimation may be corrupted by the noise related to the off-gas composition, pressure, and the gasflow rate fluctuations if additional oxygen is used enrich the aeration air. Therefore, to control thehigh-density cultivation processes, it is recommended to use CPR data in SGR estimation relationships.
A more accurate SGR control system based on OUR or CPR measurements is proposed inReferences [25,39]. Realization of the proposed control system does not require a mathematical modeland a priori knowledge of the culture of the microorganisms under control. It can be realized usingstandard programmable controllers/measurement devices and is well suited for control of industrialbiotechnological processes. In the cited contributions, it is shown that if the substrate feeding rate ismanipulated to control OUR during the process, in such a way that the OUR data-based ratio R
R =dOUR
dt1
OUR, (13)
is stabilized at the desired SGR set-point R = µset, then the specific growth rate µ will asymptoticallyapproach the set-point µset and will be controlled at that point. For control of the ratio R, the PI controlalgorithm was recommended, and controller gain was adapted to the time-varying dynamics of thecontrolled process using the gain-scheduling approach with the feeding rate as the scheduling variable.The block-scheme of the SGR control system and the simulation results of the system’s performanceare presented in Figure 3. The simulation and experimental investigation tests of the proposed SGRautomatic control system have shown a stable performance and sufficiently accurate control of the SGRunder stepwise changes to the process parameter values and high-level noise of the feedback signalmeasurements [25,39]. This control system can be efficiently applied in controlling biotechnologicalprocesses, in which the SGR set-point is constant or changes slowly. For realization of the system,either OUR or CPR online estimates can be used.
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In the next sections, more complex control systems are discussed that overcome the above shortcomings.
(a) (b)
Figure 2. Simulated trajectories of the biomass growth and carbon dioxide production rate (CPR) (a), and the trajectories of the real specific growth rate (SGR) and that estimated from Equation (7) (b) in a typical recombinant E. coli cultivation process in a 1 m3 bioreactor.
(a) (b)
Figure 3. Block-scheme of the SGR control system (a) and the simulation results of the system performance (b). Reproduced with permission from D. Levišauskas, Biotechnology Letters; published by Springer Nature, 2001.
4.3. SGR Control Systems Based on CPR/OUR Estimations and the Mass of CO2/O2 Produced/Consumed During Cultivation
Robust control of the SGR is a crucial problem when designing an efficient process, in which the SGR is to be controlled at the value μset < μmax in order to secure reproducibility of the processes. However, the already discussed SGR closed-loop control systems have two shortcomings: (a) for system implementation, an online estimation of the μ-values is required, and (b) high batch-to-batch reproducibility is not guaranteed. For example, if disturbances occur during a process (e.g., variation in the initial amount of biomass X0) or in the instrumentation (e.g., if the substrate feeding is shortly interrupted), they cause slight deviations in the biomass growth trajectory from the desired trajectory, and such an offset cannot be eliminated later on, even if the controller exactly tracks the predefined μ-profile. An approach to cope with the disturbances that cause process reproducibility problems is proposed in Reference [40]. In this work, a desired SGR time profile μset(t), the initial amount of biomass X0, and Equation (1) were used to estimate the biomass growth time profile X(t) during the process. If the biomass growth profile X(t) can be tightly controlled by manipulating the substrate feeding rate, the corresponding SGR profile will follow the desired μset(t) profile. This control system is more robust as compared to direct SGR control systems, as the short-term disturbances that occur
Figure 3. Block-scheme of the SGR control system (a) and the simulation results of the systemperformance (b). Reproduced with permission from D. Levišauskas, Biotechnology Letters; publishedby Springer Nature, 2001.
It should be stressed that the SGR control systems based on Equation (11), when applied inhigh-density cultivation processes, cause noticeable deviations at high cell concentrations. SGR controlsystems based on Equation (13) are less efficient when tracking time-varying SGR set-point profiles.
In the next sections, more complex control systems are discussed that overcome theabove shortcomings.
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4.3. SGR Control Systems Based on CPR/OUR Estimations and the Mass of CO2/O2 Produced/ConsumedDuring Cultivation
Robust control of the SGR is a crucial problem when designing an efficient process, in whichthe SGR is to be controlled at the value µset < µmax in order to secure reproducibility of the processes.However, the already discussed SGR closed-loop control systems have two shortcomings: (a) forsystem implementation, an online estimation of the µ-values is required, and (b) high batch-to-batchreproducibility is not guaranteed. For example, if disturbances occur during a process (e.g., variationin the initial amount of biomass X0) or in the instrumentation (e.g., if the substrate feeding is shortlyinterrupted), they cause slight deviations in the biomass growth trajectory from the desired trajectory,and such an offset cannot be eliminated later on, even if the controller exactly tracks the predefinedµ-profile. An approach to cope with the disturbances that cause process reproducibility problemsis proposed in Reference [40]. In this work, a desired SGR time profile µset(t), the initial amount ofbiomass X0, and Equation (1) were used to estimate the biomass growth time profile X(t) during theprocess. If the biomass growth profile X(t) can be tightly controlled by manipulating the substratefeeding rate, the corresponding SGR profile will follow the desired µset(t) profile. This control systemis more robust as compared to direct SGR control systems, as the short-term disturbances that occur inthe control equipment and the process itself are compensated by controlling an integral variable—theamount of accumulated biomass X(t). However, implementation of the above control system requiresdevelopment of a reliable soft-sensor for the online estimation of the amount of accumulated biomassduring the process. Therefore, the X(t) online estimation problem complicates the practical realizationof this control approach. To eliminate this shortcoming, simplified SGR control systems were developedand experimentally tested in bacterial and mammalian cell cultivation processes [41–43]. The mainidea behind these control systems is to use the predetermined time profiles of CPRset(t) as the system’stime-varying set-point, and the mass of CO2(t) produced during the process (mCO2set) as an indirectmetric for SGR control purposes. CPR(t) is stoichiometrically related to the SGR and the biomass(Equation (9)), and the integral of this equation gives the mass mCO2set(t) produced during the process.
By manipulating the substrate feeding rate to control the predetermined set-point time profilemCO2set(t), the control system indirectly maintains the desired SGR during the process. The structure ofthe discussed cascade control system is depicted in Figure 4. The PI controller of the inner loop controlsthe CPRset(t) time profile, and the PI controller of the outer loop controls the set mCO2set(t) profile.
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in the control equipment and the process itself are compensated by controlling an integral variable—the amount of accumulated biomass X(t). However, implementation of the above control system requires development of a reliable soft-sensor for the online estimation of the amount of accumulated biomass during the process. Therefore, the X(t) online estimation problem complicates the practical realization of this control approach. To eliminate this shortcoming, simplified SGR control systems were developed and experimentally tested in bacterial and mammalian cell cultivation processes [41–43]. The main idea behind these control systems is to use the predetermined time profiles of CPRset(t) as the system's time-varying set-point, and the mass of CO2(t) produced during the process (mCO2set) as an indirect metric for SGR control purposes. CPR(t) is stoichiometrically related to the SGR and the biomass (Equation (9)), and the integral of this equation gives the mass mCO2set(t) produced during the process.
By manipulating the substrate feeding rate to control the predetermined set-point time profile mCO2set(t), the control system indirectly maintains the desired SGR during the process. The structure of the discussed cascade control system is depicted in Figure 4. The PI controller of the inner loop controls the CPRset(t) time profile, and the PI controller of the outer loop controls the set mCO2set(t) profile.
Figure 4. Cascade control system for indirect SGR control based on predetermined CPRset(t) and mCO2set(t) time profiles.
If the controlled process is tightly kept on the CPRset(t) and mCO2set(t) time profiles, the process will also follow the desired SGR time profile. The proposed control system ensures good quality of the SGR control, and, because of the cumulative nature of the set-point variable mCO2(t), random disturbances do not significantly distort the course and reproducibility of the process.
Implementation of the proposed control system can be realized in the following steps:
• Choose a rational μset(t) time profile for the process. A proper profile can be estimated from expert knowledge, mathematical model-based process optimization results, or from the analysis of a successful “golden batch” experiment.
• Choose an appropriate inoculum size (initial amount of the total biomass X0) for the process and estimate the biomass growth time profile X(t) using the μset(t) profile, Equation (4), and a numerical integration procedure.
• Estimate the CPRset(t) time profile using Equation (10) and the identified parameter values α and β. Note that the above parameter values may be different for the biomass growth and product formation stages.
• Integrate the CPRset(t) time profile to get the corresponding profile mCO2set(t) for the controlled process.
• Control the process by tracking the estimated profiles CPRset(t) and mCO2set(t). Control is realized using the cascade control system that manipulates the substrate feeding rate.
Various realizations of the above control system have been investigated by computer simulations of the system’s performance and by controlling real processes of recombinant E. coli and mammalian cells (CHO) [18,41,42]. Typical results of the applied control system for controlling the recombinant E. coli fed-batch cultivation processes over six runs are presented in Figure 5. The laboratory-scale experimental results show that the proposed control approach leads to a stable and
Figure 4. Cascade control system for indirect SGR control based on predetermined CPRset(t) andmCO2set(t) time profiles.
If the controlled process is tightly kept on the CPRset(t) and mCO2set(t) time profiles, the processwill also follow the desired SGR time profile. The proposed control system ensures good quality ofthe SGR control, and, because of the cumulative nature of the set-point variable mCO2(t), randomdisturbances do not significantly distort the course and reproducibility of the process.
Implementation of the proposed control system can be realized in the following steps:
Processes 2019, 7, 693 10 of 14
• Choose a rational µset(t) time profile for the process. A proper profile can be estimated from expertknowledge, mathematical model-based process optimization results, or from the analysis of asuccessful “golden batch” experiment.
• Choose an appropriate inoculum size (initial amount of the total biomass X0) for the processand estimate the biomass growth time profile X(t) using the µset(t) profile, Equation (4), and anumerical integration procedure.
• Estimate the CPRset(t) time profile using Equation (10) and the identified parameter values α andβ. Note that the above parameter values may be different for the biomass growth and productformation stages.
• Integrate the CPRset(t) time profile to get the corresponding profile mCO2set(t) for the controlled process.• Control the process by tracking the estimated profiles CPRset(t) and mCO2set(t). Control is realized
using the cascade control system that manipulates the substrate feeding rate.
Various realizations of the above control system have been investigated by computer simulationsof the system’s performance and by controlling real processes of recombinant E. coli and mammaliancells (CHO) [18,41,42]. Typical results of the applied control system for controlling the recombinantE. coli fed-batch cultivation processes over six runs are presented in Figure 5. The laboratory-scaleexperimental results show that the proposed control approach leads to a stable and robust behavior ofthe controlled process. It should also be stressed that small variations in the initial amount of biomassX0 and short instrumentation disturbances do not significantly affect the reproducibility of the process.
Processes 2019, 7, x FOR PEER REVIEW 10 of 13
robust behavior of the controlled process. It should also be stressed that small variations in the initial amount of biomass X0 and short instrumentation disturbances do not significantly affect the reproducibility of the process.
(a) (b)
Figure 5. Typical experimental results of the total cumulative CPR (a) and SGR indirect control (μset(t) = 0.5 1/h at the first process stage and μset(t) = 0.175 1/h at the second stage) (b) during the recombinant E. coli cultivation process. Reproduced with permission from M. Jenzsch et al., J. of Biotechnology; published by Elsevier, 2007.
Because of the significant changes in the process dynamics during cultivation, it is possible to improve control quality of the cascade control system by adapting controller parameters. Tuning parameters of the PI controllers can be adapted to time-varying dynamics of the controlled process using the gain-scheduling approach. The controller adaptation scheme using the gain-scheduling algorithm is shown in Figure 4 by the dashed lines. In the gain-scheduling algorithms, one can use CPR or OUR measurements as the gain-scheduling variables.
Instead of using the OUR(t) or CPR(t) time profiles, the performance of the inner control loop of the cascade control system can also be improved by implementing the SGR estimator, developed from Equations (12) and (13) [44]. Investigation results presented in Reference [44] have shown that the control system with the SGR estimator outperforms the control system depicted in Figure 4 when the controlled process is affected by disturbances to the substrate feeding rate. On the other hand, implementation of the modified control system requires additional calculations related to online estimation of the SGR.
The structure of the SGR control system presented in Figure 4 may be used as a basis for development of closed-loop control systems for controlling the processes of various microbial cultures in industrial bioreactors. Because it is technically simple to implement and possible to improve batch-to-batch reproducibility, this system could be used as a benchmark to compare the control quality of various SGR control systems and to evaluate their potential implementation in industrial bioreactors.
5. Concluding Remarks and Recommendations
In recent years, numerous research papers have been published, in which original solutions and sophisticated control techniques were developed for the automatic control of microbial and mammalian cell cultivations processes. However, the majority of the proposed control systems are too complicated to be attractive for robust control of industrial biotechnological processes. Therefore, the well-known statement of Luyben [16], “Complex elegant control systems look great on paper but soon end up on ‘manual’ in an industrial environment”, is also valid for the majority of the control systems developed for biotechnological processes.
In this paper, relatively simple control approaches that can be applied in microbial and mammalian cell cultivation processes are discussed and recommended for practical application. The reviewed algorithms and systems designed for indirect control of the specific growth rate can
Figure 5. Typical experimental results of the total cumulative CPR (a) and SGR indirect control(µset(t) = 0.5 1/h at the first process stage and µset(t) = 0.175 1/h at the second stage) (b) during therecombinant E. coli cultivation process. Reproduced with permission from M. Jenzsch et al. J. ofBiotechnology; published by Elsevier, 2007.
Because of the significant changes in the process dynamics during cultivation, it is possible toimprove control quality of the cascade control system by adapting controller parameters. Tuningparameters of the PI controllers can be adapted to time-varying dynamics of the controlled processusing the gain-scheduling approach. The controller adaptation scheme using the gain-schedulingalgorithm is shown in Figure 4 by the dashed lines. In the gain-scheduling algorithms, one can useCPR or OUR measurements as the gain-scheduling variables.
Instead of using the OUR(t) or CPR(t) time profiles, the performance of the inner control loopof the cascade control system can also be improved by implementing the SGR estimator, developedfrom Equations (12) and (13) [44]. Investigation results presented in Reference [44] have shown thatthe control system with the SGR estimator outperforms the control system depicted in Figure 4 whenthe controlled process is affected by disturbances to the substrate feeding rate. On the other hand,
Processes 2019, 7, 693 11 of 14
implementation of the modified control system requires additional calculations related to onlineestimation of the SGR.
The structure of the SGR control system presented in Figure 4 may be used as a basis fordevelopment of closed-loop control systems for controlling the processes of various microbial culturesin industrial bioreactors. Because it is technically simple to implement and possible to improvebatch-to-batch reproducibility, this system could be used as a benchmark to compare the control qualityof various SGR control systems and to evaluate their potential implementation in industrial bioreactors.
5. Concluding Remarks and Recommendations
In recent years, numerous research papers have been published, in which original solutions andsophisticated control techniques were developed for the automatic control of microbial and mammaliancell cultivations processes. However, the majority of the proposed control systems are too complicatedto be attractive for robust control of industrial biotechnological processes. Therefore, the well-knownstatement of Luyben [16], “Complex elegant control systems look great on paper but soon end up on‘manual’ in an industrial environment”, is also valid for the majority of the control systems developedfor biotechnological processes.
In this paper, relatively simple control approaches that can be applied in microbial and mammaliancell cultivation processes are discussed and recommended for practical application. The reviewedalgorithms and systems designed for indirect control of the specific growth rate can significantlyincrease robustness and batch-to-batch reproducibility of industrial-scale biotechnological processes.The recommended control algorithms and systems are based on CPR or OUR online measurementsand on the total mass of oxygen consumed or the total mass of carbon dioxide produced during theprocess. In the case when additional oxygen is used during the processes, it is recommended to usethe CPR and mCO2 signals in the control system algorithms because of their lower estimation errorscompared to those when using OUR and mO2 signals. To estimate oxygen uptake and carbon dioxideproduction rates, several industrially well-established gas analyzers and mass flow meters are available.Basic instrumentation for installation of the SGR control systems, the online gas analyzer, combinesparallel measurement of CO2 and O2 concentrations in the off-gas using two space-saving sensors.The analyzer can be used both for lab- and industrial-scale bioreactors. In the industrial gas analyzers,compensators for gas pressure and humidity are incorporated. Consequently, these analyzers ensuregood precision and reliability of the measurements.
The available instrumentation and discussed control methods and systems provide a possibilityfor wider application of SGR control in biotechnological processes. At the very beginning of theprocess, accuracy of the indirect measurements is usually low and insufficient to track exactly theSGR set-point time profile in closed-loop control systems. Consequently, it is recommended to startthe process using the feeding rate open-loop control strategies determined by Equations (8) and (9)and, after three to four hours, to switch the SGR control to the closed-loop control system. For thelow-density cell cultivation processes, the SGR control system based on Equation (11) is recommended.For processes, in which the SGR set-point is kept constant, the control system based on Equation (13)(Figure 3) is well suited. For more advanced applications, the SGR control system presented in Figure 4is recommended. The above system can be applied as a benchmark to compare the control quality ofvarious SGR control systems.
Author Contributions: Conceptualization, V.G., R.S., and D.L.; methodology, V.G., R.S., and D.L.; software, V.G.and R.S.; validation, V.G., R.S., and D.L.; formal analysis, V.G., R.S. and D.L.; investigation, V.G., R.S., D.L.,and R.U.; resources, V.G., R.S. and D.L.; data curation, V.G.; writing—original draft preparation, V.G. and R.S.;writing—review and editing, V.G., R.S., D.L., and R.U.; visualization, V.G.; supervision, V.G.; project administration,V.G.; funding acquisition, V.G., R.S., D.L., and R.U.
Funding: This research was funded by the European Regional Development Fund according to thesupported activity “Research Projects Implemented by World-class Researcher Groups” under the MeasureNo. 01.2.2-LMT-K-718.
Conflicts of Interest: The authors declare no conflicts of interest.
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